Classification of ordered texture images using regression modelling and granulometric features

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Khatun, Mahmuda and Gray, Alison and Marshall, Stephen (2011) Classification of ordered texture images using regression modelling and granulometric features. In: Irish Machine Vision and Image Processing Conference, 2011-09-08 - 2011-09-09. (https://doi.org/10.1109/imvip.2011.20)

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Abstract

Structural information available from the granulometry of an image has been used widely in image texture analysis and classification. In this paper we present a method for classifying texture images which follow an intrinsic ordering of textures, using polynomial regression to express granulometric moments as a function of class label. Separate models are built for each individual moment and combined for back-prediction of the class label of a new image. The methodology was developed on synthetic images of evolving textures and tested using real images of 8 different grades of cut-tear-curl black tea leaves. For comparison, grey level co-occurrence (GLCM) based features were also computed, and both feature types were used in a range of classifiers including the regression approach. Experimental results demonstrate the superiority of the granulometric moments over GLCM-based features for classifying these tea images.

ORCID iDs

Khatun, Mahmuda, Gray, Alison ORCID logoORCID: https://orcid.org/0000-0002-6273-0637 and Marshall, Stephen ORCID logoORCID: https://orcid.org/0000-0001-7079-5628;
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